Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates

Statistical Relational Learning (SRL) combines the beneﬁts of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). We propose a new SRL algorithm, GleanerSRL, to generate the probability that an example is positive within highly-skewed relational domains. In this work, we combine clauses from Gleaner, an ILP algorithm for learning a wide variety of ﬁrst-order clauses, with the propositional learning technique of support vector machines to learn well-calibrated probabilities. We ﬁnd that our results are comparable to SRL algorithms SAYU and SAYUVISTA on a well-known relational testbed.